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This paper describes two exemplary projects on physical ROS-compatible robots (i.e., Turtlebot3 Burger and Waffle PI) for an undergraduate robotics course, aiming to foster students’ problem-solving skills through project-based learning. The context of the study is a senior-level technical elective course in the Department of Computer Engineering Technology at a primarily undergraduate teaching institution. Earlier courses in the CET curriculum have prepared students with programming skills in several commonly used languages, including Python, C/C++, Java, and MATLAB. Students’ proficiency in programming and hands-on skills makes it possible to implement advanced robotic control algorithms in this robotics course, which has a 3-hour companion lab session each week. The Robot Operating System (ROS) is an open-source framework that helps developers build and reuse code between robotic applications. Though mainly used as a research platform, instructors in higher education take action in bringing ROS and its recent release of ROS 2 into their classrooms. Our earlier work controlled a simulated robot via ROS in a virtual environment on the MATLAB-ROS-Gazebo platform. This paper describes its counterparts by utilizing physical ROS-compatible autonomous ground robots on the MATLAB-ROS2-Turtlebot3 platform. The two exemplary projects presented in this paper cover sensing, perception, and control which are essential to any robotic application. Sensing is via the robot’s onboard 2D laser sensor. Perception involves pattern classification and recognition. Control is shown via path planning. We believe the physical MATLAB-ROS2-Turtlebot3 platform will help to enhance robotics education by exposing students to realistic situations. It will also provide opportunities for educators and students to explore AI-facilitated solutions when tackling everyday problems. 

Abstract Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution three-dimensional (3D) structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3D reconstruction, known as Kam’s method, relies on the moments of the two-dimensional (2D) images. Inspired by Kam’s method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate the uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity.